Package index

All functions

DLR()

Estimating the Capacity for Improvement in Diagnostic Risk Prediction with an additional marker based on the Diagnostic Likelihood Ratio (DLR)

Paired1

DTComPair-dataset 1

acc.1test()

Accuracy of a Single Binary Diagnostic Test

acc.paired()

Accuracy of Two Binary Diagnostic Tests in a Paired Study Design

dlr.regtest()

Differences in Diagnostic Likelihood Ratios

DTComPair-package DTComPair

Comparison of Binary Diagnostic Tests in a Paired Study Design

ellipse.pv.rpv()

Elliptical joint confidence region for relative positive and negative predictive value

generate.paired()

Generate Dataset from “tab.paired”-Object

print(<acc.1test>)

Print “acc.1test”-Object

print(<acc.paired>)

Print “acc.paired”-Object

print(<tab.1test>)

Print “tab.1test”-Object

print(<tab.paired>)

Print “tab.paired”-Object

pv.gs()

Generalized Score Statistic for Comparison of Predictive Values

pv.prev()

Compute predictive values for theoretical prevalences

pv.rpv()

Comparison of Predictive Values using Relative Predictive Values

pv.wgs()

Weighted Generalized Score Statistic for Comparison of Predictive Values

read.tab.1test()

Read in “tab.1test”-Objects

read.tab.paired()

Read in “tab.paired”-Objects

represent.long()

Long Representation of Results from Two Binary Diagnostic Tests

sesp.diff.ci()

Confidence Intervals for Differences in Sensitivity and Specificity

sesp.exactbinom()

Exact Binomial Test for Differences in Sensitivity and Specificity

sesp.gen.mcnemar()

Generalized McNemar's test

sesp.mcnemar()

McNemar Test for Comparison of Sensitivities and Specificities

sesp.rel()

Comparison of the accuracy of two tests using relative sensitivity and specificity

tab.1test()

Tabulate Single Binary Diagnostic Test vs. Gold-Standard

tab.paired()

Tabulate Results from Two Binary Diagnostic Tests in a Paired Study Design

tpffpf.rel()

Comparison of the accuracy of two tests using relative true positive and false positive fraction